To Infinity and Beyond - Becoming a Better DopeyBadger (Comments Welcome)

Hopefully it will work, but if it doesn't ... I wouldn't necessarily write off the idea of medication. Sometimes medication is permanent, but sometimes it isn't - sometimes it can just create a symptom-free (or lower symptom) environment for you to start experimenting with those other changes. Eventually you may get to a point where the environmental and lifestyle changes are enough that you don't need the medication anymore.

I think that's the progression idea I've got. Try to make changes in lifestyle, if that fails, then start meds, and continue changes. And maybe if things improve eventually find a way where I wouldn't have to take the meds with permission and proper weaning off the meds.
 
One of my favorite things about running is the research behind it. I love love love to break down articles or papers to read more in-depth into what they're trying to say. @JeffW posted this article in the running thread and I couldn't resist digging into it.

Paging @DopeyBadger

I was thinking of posting this in your thread about creating training plans, but thought the greater Running Thread community may be interested. I found it an interesting discussion about race time prediction data and correlations to various factors.

https://www.theguardian.com/lifeand...-updated-formula-for-marathon-running-success

Ian Williams: An updated formula for marathon-running success

This is another attempt by someone in trying to optimize the marathon race equivalency calculation portion. There's little doubt that the 5k, 10k, and HM relationships are strong. But the M sits atop the mountain with a difficulty unlike the others in properly estimating finish time from other race distances previously completed. Why is it even important to have a good race equivalency going into a race day? Well, running a marathon can literally come down to a few seconds per mile vs best performance and literal blow-up. It all comes down to the physiological difference between the M and the other races. Once you pass a certain threshold the ticking time bomb that is pace will starting counting down. And unless you pace perfectly, things can go haywire quick. So a good race equivalency or honest assessment of race day goal pace can be extremely beneficial. The classic formula used in most online calculators is Peter Rigel's formula:

M = HM x 2^1.06

Which means your M is 2.08 times slower than your HM.

I've previously reviewed a new-age calculation from Vickers (link).

So let's dive right into Ian Williams attempt at adjusting the classic marathon race equivalency calculator.

Sample size - 1071 different HM to M relationships. Good, but about half the size of Vickers data set (although Vickers used 5k, 10k, and HM performances). Williams did cut the data set to runners who had completed at least 5 HM and Ms, thus more experienced runners who knew what they were getting themselves into.

Sample collection - :crazy2: An internet "logging system" open to anyone using fetcheveryone.com to find participants. The article does not speak to potential issues of representativeness and selection bias. I'm not terribly concerned about the selection bias. There is literally no data as to whether this data set resembles a normal population set (male/female/age/training history/representative finishing times, etc.). I have reason to believe that the majority of William's data set is from runners at 2:00 half marathon or less (based on the displayed data and groups he chooses to display). The male median time in this study was UNK for the marathon versus 4:11 for NYC marathon, and 4:16 for Running in the USA. The female median time in this study was UNK, 4:38 in NYC, and 4:41 in Running in the USA. So my best guess on what I can surmise from the data set is that while the median national time is close to 4:16-4:41 in the US, very little of this data set (if at all) was based on runners around or slower than the national average.

I can't tell initially from the article whether the data is logged daily or just once at the end. That would call into question the chance for error. More measurements would reduce the chance for error. If you've got the entire data set (like a Strava history), then everything is there. But if the dataset Williams used relied solely on self-reporting, then it could make for a much higher chance for error.

Also, I can't tell if this is recent HM vs recent M. Or if it is PR HM vs recent M.

Alight, so let's dive in!

As previously stated, Rigel is:

M = HM x 2^R

where R=1.06

Williams sets out to redefine R with a new value that makes the calculator more accurate for more people.

Williams starts by using his dataset of 1071 runners to define the relationship between their HM and M performances.

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The very first thing that sticks out to me - no y-axis defined. What exactly am I looking at here? It would appear to be a histogram or distribution plot of the relationship of the 1071 runners HM to M. 1.06 represents the current Rigel. Williams proposed 1.15 is a better R value since it falls further towards the middle. I would not deny that either based on the graph. It certainly appears the 1.15 falls much closer to the middle than 1.06. And if being conservative on pacing for the marathon is an important variable (which I believe it is), then being on the slower side for predicting won't prevent a great marathon performance (because you can negative split the back half of the race). But I wasn't satisfied having no y-xais. So I made one for him:

Screen Shot 2018-02-15 at 8.51.33 PM.png

I actually used photoshop to measure the height of each of his bars. Then I assumed this shown data set represented the whole 1071 runners. Which may or may not be the case. I don't believe anyone is faster than 1.01, but slower than 1.30 is certainly possible. Although, I certainly don't know. I feel relatively confident because the total height of the bars added together was 49.79 or very very close to a whole number of 50. That means I could calculate the number of runners per bar:

Screen Shot 2018-02-15 at 8.56.50 PM.png

So when I look back at the 1.01 bar, it really represents 0.25% of the population or a guess of 2.7 runners. Makes sense. Only 3 runners out of 1071 were able to hit a 1.01 R value. So, does my data extraction work? Well Williams states in the article that less than 5% of the runners had a R of 1.06. His other linked article says 49 total runners at 1.06 or less. That jives closely with what I've got. Remember mine are in bars of 1.06. But that probably really means 1.055 to 1.064. So the numbers will be off slightly, but not terribly. So keep in mind when the data set talks about runners at exactly 1.06, it's really only talking about 29 total runners. A much much much smaller data set suddenly.

But what does that mean in actual time conversions?

Screen Shot 2018-02-15 at 8.58.24 PM.png

So for example, someone with a R of 1.01 with a HM time of 2:00:00 was able to run a M in 4:01:40. For someone after 5 HM/Ms to run a virtual identical pace between their HM and M is astounding. Almost too astounding... That brings up another question about the dataset. The relationship between HM and M can't be viewed under a microscope. There are variables of race day that matter so much for performance. Race crowding, elevation, and weather just to name a few. If someone is running a uphill HM in hot weather in 2:00:00 and then a downhill cold weather M in 4:01:40, then the data starts making more sense. Regardless, it's another reason to cast question on this. Vickers did a better job attempting to correct this. So since Vickers is such a great guy and released his dataset to the public we can map Vickers dataset in the same manner as Williams. Vickers has a total of 862 runners in his dataset (including what I believe is a slower median population meaning it is more representative of the US population of marathon runners) that have matching HM and M condition races (and if not matching than an adjustment was used).

Screen Shot 2018-02-16 at 8.13.25 AM.png

Hooray! I'd say for the most part the datasets follow a similar trend. Not the same, but similar.

So the initial conclusion was 1.15 is a better predictor R for HM to M than is 1.06. It does split the middle of the data set (with 47% on both sides). So better. Williams dataset says the midpoint is 1.15 with a 25-75% range of 1.10 to 1.19 and Vickers dataset says the midpoint is 1.13 with a 25-75% range of 1.09-1.17.

So for a 2:00 HM runner, what does that mean?

Rigel - traditional calculator (1.06) = M of 4:10:12
Williams - 1.15 = M of 4:26:18 (range of 4:17-4:33)
Vickers - 1.13 = M of 4:22:38 (range of 4:15-4:30)

Since you are likely to see a better performance in the marathon with a conservative start, this new value of around 1.13-1.15 looks good to me. Slower is better at the beginning so you can leave some room for error in the second half of the race. Go out too fast in the beginning and the risk of blowing up is much much higher.

The problems start to arise when he starts to parce the data apart to make other conclusions about training in general that leads to performance.

Does gender matter?

Matches what I've read before. Women are better pacers during a marathon (more even/negative splits and less positive splits, (or faster at the end)), hypothesized that women are better at burning fat then men, and hypothesized that women are better at dissipating heat than men. So if a woman and a man have equal HM times going into the M, the woman is more often than not going to beat the man.

So I agree with the conclusion.

Are faster runners better?

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The bottom grey line represents the top 10% of runners with that HM time in each subset of data. So Williams pieced apart the dataset into secondary pools with HM times of 1:20, 1:25, 1:30, 1:35, 1:40, etc. Given the relative smoothness of the line we can tell this is the case. Remembering back, there are only 67 total runners with a 1.06 or less in the dataset of 1071. There are only 256 with a 1.10 or less. There appear to be 9 subsets of data. As would make sense, there are likely fewer runners in the dataset at 1:20-1:30, then there is at 1:50-2:00 (if this dataset is anything like a normal population of HM runners). So the data at the beginning of the line is probably based off very few runners.

The first thing that jumps out to me is that the relationship between HM time and R (for M) is pretty equal for the top 10% across all HM times. A 1:20 10% runner is around 1.06, but so is a 1:55 runner. And the difference between the two is quite small anywhere in-between.

So the variation of the mean is not coming from the top 10% becoming worse converters, but the bottom portion of the population as the HM time slows are getting worse at being converters. So the question would follow, what are the top 10% runners doing that are all near 1.06 across all HM times that the bottom 10% are not? Seems to suggest that regardless of HM time you can be a good converter if you're doing the right things in training. And those in the slower HM times tend to have more runners doing the wrong thing in training (hence bad converters).

What about training mileage?

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So per Williams this graph is the "typical" amount of miles run by experienced marathon runners (not their first) going for a PR marathon attempt. This does not have to be the same dataset he used to create the previous graph, but rather a measuring stick he created. So this original dataset doesn't have to be correlated with success in any way or being a good converter.

So the graph on the surface tells a story that most of us know. The people with faster marathon finishing times run more miles. But you know me, I don't like to look at miles, I like duration. So if I were to standardize these mileages across each subset by either Marathon Pace or EB pace (which tends to be the average pace I schedule runners at or 1.12 times slower than MP), then what does the dataset look like?

Screen Shot 2018-02-16 at 8.43.32 AM.png

A 2:20 runner runs 1200 miles in 16 weeks. The MP of 2:20 is a 5:21 min/mile. If the 2:20 runner were to average MP for the 16 weeks of training, then they would do 6:40 hours of training per week (or 106 hours total). If we instead used EB, then the 2:20 runner averages 7:28 hours per week. The 2:20 is clearly the outlier, because look at the other subsets of data. The 2:40, 3:00, 3:20, 3:40, 4:00, 4:20, and 4:40 all run about 5:00 hours (if at MP) or 5:30 hours (if at EB) per week. So on the surface the 2:40 to 4:40 runners would appear different, but when taking into account their relative training pace, they're all actually very similar. This comes down to training load and why I like to evaluate training plans by time moreso than mileage. Two runners doing 80% of training at easy with 9 hours of total running per week will be reaping similar training benefits regardless if one runs a 2:20 M and the other a 4:40 M.

For reference, the marathon training plans I write tend to be in the 7-8 hours average range for 16 weeks. So my plans are like the outliers in the 2:20 M time group.

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This is a hard graph for me to interpret. Based on the shape and description, I believe this is a cumulative graph. Meaning that once a runner has been passed in the data set it continues to get counted. So a runner in the 1.06 success portion means that 12% of runners who have sufficient mileage achieve a 1.06. And 60% of runners with sufficient mileage achieve a 1.15 OR LESS. Since the graph does not go down EVER, I don't believe the interpretation of the graph is when r=1.15 is achieved 60% of runners with a 1.15 had sufficient mileage because for that to be the case the addition of insufficient and sufficient on the graph should always equal 100%.

Here's where the interpretation of the graph gets tricky for me. Going back up to the original dataset, there are 580 runners who achieved a 1.15 or better (or 54.19% of the dataset). A total of 60% of runners with sufficient mileage ran 1.15. So the sufficint mileage group and the total group are 60% vs 54.2%. Seems to me these are not very far off from each other. Using this information, I should be able to calculate the number of runners in the 1071 dataset with sufficient mileage and insufficient mileage. I'll save the math, but it comes down to 820 runners have sufficient and 251 runners had insufficient. That allows a 60% success rate in sufficient and 35% success rate in insufficient while maintaining a total of 580 runners in the total dataset.

So going back to 1.06 then, we have 67 total runners at 1.06 OR LESS. From the graph, approximately 12% of the sufficient group hits a 1.06 vs ~5% for insufficient. So, how does that look in the raw numbers? Well that's where I can't make sense of it. If 12% of 820 runners are successful at 1.06 OR LESS, then I've got 98.4 runners. But only 67 runners in the whole data set were successful at 1.06 or LESS. So my original interpretation can't be right, can it? Therefore, I'm confused on this one.

I believe the basic premise is correct, those who run more tend to be more successful. But I can't figure out how to interpret this graph.

What about long runs?

A common consideration for marathon training plans is the long run.

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Can't say I've ever heard of the 5L = 100 mile rule of thumb. Where the 5 longest runs in a 16 week plan summed together equal over 100 miles is a good sign. Again, I standardized this information by time:

Screen Shot 2018-02-16 at 9.31.20 AM.png

If a 2:20 runner does 110 miles, then they are averaging 22 miles per Long Run. If the pace is MP, then they are doing it in 1:57:33. If the pace is LR pace (roughly 8% slower than MP), then it's duration is 2:07. So the faster runners, tend to do less total duration on their longest run cumulatively over the course of the plan. Sounds about right to me. I'm of the mindset that the cutoff should be around 2:30 for a training run duration limit at LR pace. Seems like the runners doing 2:20-3:00 marathon times are in that range. And many of the runners doing up to 2:45+ are in the 3:20 or slower M time range. So faster runners are spending less total time in any single training run.

So where the amount of time spent training was near equal across the board, the same doesn't appear for the 5L evaluation. On a training plan like mine, where does 5L typically fall?

Screen Shot 2018-02-16 at 9.37.58 AM.png

Since it's based on time, I've got the MP and LR paces for different paced M finish times. I then calculated the peak of training as 2:30 duration limit. So a 3:20 runner will max at 18 miles and a 4:40 runner at 13 miles. Then, I like to hit peak only twice during a plan and then reduce every previous "high" week by one mile. So for me, a 2:20 runner would be doing 124 miles as 5L (higher than the 110 from Williams dataset) and a 4:00 runner would do 70 miles (or far lower than the 95 miles in Williams dataset).

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Now, since all of the runners 5Ls are pooled together, I can't evaluate this graph by duration. But I can point out something troubling to me. The grey lines again represent top and bottom 10%. I already showed reasonably well that my assumed dataset matched Williams graphed dataset. Yet, I estimate he has maybe 10 to 11 total runners out of 1071 above or at 1.30 R. This graph shows the bottom 10% of 85, 90, 95, 100, and 110 at or higher than 1.30. How can that possibly be when there are only 10 to 11 runners in this area? Another new dataset? Confused again.

What I do get from this graph is that a difference of 85 (17 mile avg) vs 100 (20 mile avg) yields an R difference of 1.15 vs 1.21. For a 2:00 HM runner, that's 4:26 vs 4:37 (4% diff). Not an insignificant difference, but not as big a difference as the "are faster runners better" difference which was more like 1.10 vs 1.20 from faster runners to slower runners (5-7% difference). So something other than 5L plays a bigger role in predicting good converters vs bad ones.

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So I can take this graph one step further. Williams gives data on 16 week training mileage and 5L from 16 weeks. Which means I can calculate his subset data's % by Marathon time.

Screen Shot 2018-02-16 at 9.51.11 AM.png

The 2:20 runners had a 5L of 110 and 16 week total of 1200 miles. Therefore, their %5L of total was 9.2%. So not only are the better converters around 10% of total, but so are the faster runners. It's possible then to think that if one were to train like a faster runner/better converter they could achieve a lower R (and better M time relative to HM performance). So balance is important. I preach that a ton. So it's not the total mileage of the 5L that matters near as much as the % of which 5L makes up the total plan. So spend less time on the long run, and more time spent training during the week.

So where do my plans fall?

Screen Shot 2018-02-16 at 9.53.44 AM.png

As covered previously, a 5L for me for a 3:00 runner will be around 95 miles. They'll do about 7 hours of training on average regardless of current fitness level. Their pace will be around EB (1.12x slower than MP) as an average for the plan. Therefore, we can calculate the average mileage and total mileage for each subset underneath my scheme. This comes out to a nearly identical 11% 5L as a % of the total training mileage across the board. So my plans are closer to the R values of 1.06-1.07 (or my training plans are better representative of runners who tend to get faster M times relative to their HM times).

What about training pace?

In my book, it's pretty darn important. Pace matters more than mileage, because to me mileage is just a function of time and pace spent training.

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Unsurprising to me, runners at the faster paces actually train far slower than final race pace. I hark on this all the time. It suggests that if someone were to slow down in training, they too might yield better race results (or be faster).

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An interesting graph. I interpret this to mean that until your average is about 40 seconds slower than race pace, you are more likely to run slower than a 1.15 conversion then you are to run faster than it. Those who run too fast in training tend to be the ones who run worse relative performances against their HM times. So, train slower! Sure seems like somewhere between 40-70 seconds is a sweet spot. There aren't actually that many runners at 80+ seconds, but those who do are pretty successful relatively on achieving a less than 1.15 R value.

So what about my plans?

Screen Shot 2018-02-16 at 10.03.24 AM.png

According to Williams, runners with a race pace of 6:00 tend to run on average 72 seconds slower. So they'd be doing about a 7:12 average. Those at 8:00 with 35 seconds slower, at 8:35. For me, my training plans nearly always equal EB which is 1.12x slower than MP. So a 6:00 runner would average 6:43 and a 8:00 runner a 8:58. So my time differential across the board falls between 40-72 seconds. Going back to the graph Williams presented and that just so happens to appear as the sweet spot for beating the R of 1.15 (or being a better converter and achieving a faster M relative to HM performance).

On being rested

And then there's this graph....

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This appears to be saying that peak mileage is reached in "x" week of the 16 weeks. Not surprising to see that the tallest bar is 13 weeks of the traditional taper (3 weeks out). Using the y-axis I can determine how many runners peak at either 13, 14, or 15 weeks of the training plan. It is about 170. The total dataset is 1071 runners. Problem is, when I run through the numbers I only get ~680 total runners, not 1071 runners. Where did the rest of the data set go???

But even ignoring that, the alarming part is this. The traditional taper is 3 weeks. Some do 4 weeks and others 2 weeks. But in this specific dataset there are a huge (roughly 65%) number of runners doing the taper at 5 weeks out or MORE??? Some hitting highest mileage week in Week 1? And not just a few people, but 3% of this graph's population. That seems astoundingly high. Maybe they did 10 miles every week for 16 weeks and thus hit their max mileage in week 1, but that seems odd to me from a dataset standpoint.

Conclusions

The conclusions we can draw from this:

-If HM performance is equal, women are likelier to finish with a faster M time than men.
-Runners of all abilities are capable of a 1.06 or less, and roughly the top 10% of all subgroups from 1:20 HM'ers to 2:00 HM'ers were roughly the same R value (or relative performance).
-Faster runners are better converters with a lower R overall average. Makes sense then why Rigel came up with 1.06 since the elite runners available to him would have been a similar pool to the faster runners in Williams dataset.
-Runners on the slower side of the HM performances tend to have more variability as a group because of the bad converters in their groups, not because of the lack of good converters. So more people on the slower side of HM performance training inappropriately for marathon performance.
-Roughly 5:00 to 5:30 hours per week on average for a marathon training plan is considered "typical" or "sufficient" by Williams.
-Those who run more than 5:00-5:30 hours per week are more successful at being good converters than are runners who run less than 5:00-5:30 hours per week.
-Those who do 5L around 100 barely appear different than those around lesser or higher numbers. The 5L would suggest it is lower on the predictive nature than other variables.
-Those who have 5L be a lower % of total mileage from 16 weeks tend to be the best converters. The faster runners also tend to be the ones with lower %5L values. Relying less on the long runs and more balance yields a better relative performance.
-Those who train at 40-80 seconds slower than race pace more often than not will be a good converter and have a R less than 1.15.

For my marathon training plans:

-The training load I schedule (around 7 hours per week) is sufficient (above 5-5.5 hrs) and is most like a 2:20 marathon runner's training plan.
-Almost none of my training plans would hit the 100 mile rule of thumb 5L. Most would be far far lower. The data suggests this is a minimal variable compared to other things.
-The %5L of training plans is a very good predictor of being a good converter. My plans are about 11% 5L of the total regardless of ability levels. The best converters (1.06-1.07) are around 9-10%. The worst converters (1.17-1.18) are around 20-21%.
-My training plans average pace is between 40-70 seconds depending on one's relative fitness. The point at which you are more likely to achieve a conversion better than 1.15 than not, is between 40-70 seconds. Or exactly where I schedule my paces.

This explains why most of my marathon training plans yield a final marathon time very close to my prediction. They check off all the boxes for optimal race day performance based on Williams conclusions. My predictions between HM and M performance is 4% or almost exactly a value of 1.06. So my runners tend to achieve in the top 25% of relative performances or at around 1.10 or less for an R value.

So a good marathon plan is:
-Over 5-5.5 hrs in duration per week on average for 16 weeks.
-Has a 5L% of 9-11%. So if you do 100 miles as 5L (or five 20 milers), then you better be doing 1000 miles in the 16 weeks of training (or 63 miles per week on average). The more you diverge from this, the worse your HM conversion becomes. Although, you can still be successful at a lower 5L like 60 miles if the 5L% is still in the 9-11% range (or 600 miles total and 38 miles per week) as long as that duration is over 5-5.5 hours for your paces.
-Has you training at roughly 40-80 seconds slower on average for the plan than marathon race pace.

That was fun! Alright, that's what I see. What do you think?
 
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Well the dermatologist was on the same line of thinking as I was for the hand condition. She thought it was an uncommon display of Raynaud's with popping blood vessels in the hands (petechiae). So nothing too nefarious (Hooray!). Likely caused by my personal ability to tolerate the cold when running and not running more than my body can. And even though my hands feel hot and sweaty inside the gloves, they're likely still too cold. Warming up the rest of my body is key as well in preventing occurrences. So definitely things to focus on when I can return to running. She did prescribe me some meds (penta..? something). When I went to get it at the pharmacy the pharmacist assistant was explaining to me that the medication can lower the HR or blood pressure of people taking it. Makes sense since calcium channel blockers are meant for people with hypertension and other high blood pressure issues. Problem is, I've already got a low HR (40s and 50s) and I don't really want to see that drop even more. So I brought that up with the pharmacist and they agreed that the medication prescribed would not be a good idea. Consulted with the dermatologist and prescribed Amlodipine instead (apparently wouldn't lower HR as much). I'm not quite sold on it either. Medications are one of those things that once you start you have to keep taking it. I have to ask myself at this point is it really necessary? I haven't even tried to incorporate better measures in my daily life to avoid cold exposure or quick changes in temps. I only have had maybe 8-10 attacks in the winter, not 5-10 per week like some I read on meds for Raynauds. So I think for me at the moment, I want to see if I can make changes in my life that can help control these situations before I go the medication route. Thankfully at this point it seems I've got all my answers: Low grade stress fracture of the tibia, Raynaud's and petechiae secondary to Raynaud's.

I've been on a calcium-channel blocker for some years, and even though my resting HR is about 50 BPM, I haven't noticed significant problems with it. Obviously, you'll have to see. Also, check with the doc, because calcium-channel blockers usually work relatively quickly and don't depend on long-term exposure/build-up. What that means, is that if you know there's a cold snap coming through in the next week, you can decide to start taking them for that week to help prevent an attack, and then stop after that. This past summer, since it was so warm, I stopped taking mine for a few months and then started up when the temperatures started getting colder again. In that sense, they are relatively flexible and not really a long-term commitment like some meds. (Also during the time I was off, I was curious about if going off the meds would affect my RHR, and no noticeable affect.)

Glad you have things figured out overall. I had never heard of the petechiae related to Raynaud's, so that is interesting in the scientific sense. A lot of the long-term living with it just means figuring out what works for you. It will give you something to experiment with while your stress fracture is healing - I expect graphs and tables to be posted. :) Perhaps you will have to develop a scale to indicate the severity of an attack?
 
One of my favorite things about running is the research behind it. I love love love to break down articles or papers to read more in-depth into what they're trying to say. @JeffW posted this article in the running thread and I couldn't resist digging into it.



Ian Williams: An updated formula for marathon-running success

This is another attempt by someone in trying to optimize the marathon race equivalency calculation portion. There's little doubt that the 5k, 10k, and HM relationships are strong. But the M sits atop the mountain with a difficulty unlike the others in properly estimating finish time from other race distances previously completed. Why is it even important to have a good race equivalency going into a race day? Well, running a marathon can literally come down to a few seconds per mile vs best performance and literal blow-up. It all comes down to the physiological difference between the M and the other races. Once you pass a certain threshold the ticking time bomb that is pace will starting counting down. And unless you pace perfectly, things can go haywire quick. So a good race equivalency or honest assessment of race day goal pace can be extremely beneficial. The classic formula used in most online calculators is Peter Rigel's formula:

M = HM x 2^1.06

Which means your HM is 2.08 times slower than your M.

I've previously reviewed a new-age calculation from Vickers (link).

So let's dive right into Ian Williams attempt at adjusting the classic marathon race equivalency calculator.

Sample size - 1071 different HM to M relationships. Good, but about half the size of Vickers data set (although Vickers used 5k, 10k, and HM performances). Williams did cut the data set to runners who had completed at least 5 HM and Ms, thus more experienced runners who knew what they were getting themselves into.

Sample collection - :crazy2: An internet "logging system" open to anyone using fetcheveryone.com to find participants. The article does not speak to potential issues of representativeness and selection bias. I'm not terribly concerned about the selection bias. There is literally no data as to whether this data set resembles a normal population set (male/female/age/training history/representative finishing times, etc.). I have reason to believe that the majority of William's data set is from runners at 2:00 half marathon or less (based on the displayed data and groups he chooses to display). The male median time in this study was UNK for the marathon versus 4:11 for NYC marathon, and 4:16 for Running in the USA. The female median time in this study was UNK, 4:38 in NYC, and 4:41 in Running in the USA. So my best guess on what I can surmise from the data set is that while the median national time is close to 4:16-4:41 in the US, very little of this data set (if at all) was based on runners around or slower than the national average.

I can't tell initially from the article whether the data is logged daily or just once at the end. That would call into question the chance for error. More measurements would reduce the chance for error. If you've got the entire data set (like a Strava history), then everything is there. But if the dataset Williams used relied solely on self-reporting, then it could make for a much higher chance for error.

Also, I can't tell if this is recent HM vs recent M. Or if it is PR HM vs recent M.

Alight, so let's dive in!

As previously stated, Rigel is:

M = HM x 2^R

where R=1.06

Williams sets out to redefine R with a new value that makes the calculator more accurate for more people.

Williams starts by using his dataset of 1071 runners to define the relationship between their HM and M performances.

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The very first thing that sticks out to me - no y-axis defined. What exactly am I looking at here? It would appear to be a histogram or distribution plot of the relationship of the 1071 runners HM to M. 1.06 represents the current Rigel. Williams proposed 1.15 is a better R value since it falls further towards the middle. I would not deny that either based on the graph. It certainly appears the 1.15 falls much closer to the middle than 1.06. And if being conservative on pacing for the marathon is an important variable (which I believe it is), then being on the slower side for predicting won't prevent a great marathon performance (because you can negative split the back half of the race). But I wasn't satisfied having no y-xais. So I made one for him:

View attachment 302701

I actually used photoshop to measure the height of each of his bars. Then I assumed this shown data set represented the whole 1071 runners. Which may or may not be the case. I don't believe anyone is faster than 1.01, but slower than 1.30 is certainly possible. Although, I certainly don't know. I feel relatively confident because the total height of the bars added together was 49.79 or very very close to a whole number of 50. That means I could calculate the number of runners per bar:

View attachment 302702

So when I look back at the 1.01 bar, it really represents 0.25% of the population or a guess of 2.7 runners. Makes sense. Only 3 runners out of 1071 were able to hit a 1.01 R value. So, does my data extraction work? Well Williams states in the article that less than 5% of the runners had a R of 1.06. His other linked article says 49 total runners at 1.06 or less. That jives closely with what I've got. Remember mine are in bars of 1.06. But that probably really means 1.055 to 1.064. So the numbers will be off slightly, but not terribly. So keep in mind when the data set talks about runners at exactly 1.06, it's really only talking about 29 total runners. A much much much smaller data set suddenly.

But what does that mean in actual time conversions?

View attachment 302703

So for example, someone with a R of 1.01 with a HM time of 2:00:00 was able to run a M in 4:01:40. For someone after 5 HM/Ms to run a virtual identical pace between their HM and M is astounding. Almost too astounding... That brings up another question about the dataset. The relationship between HM and M can't be viewed under a microscope. There are variables of race day that matter so much for performance. Race crowding, elevation, and weather just to name a few. If someone is running a uphill HM in hot weather in 2:00:00 and then a downhill cold weather M in 4:01:40, then the data starts making more sense. Regardless, it's another reason to cast question on this. Vickers did a better job attempting to correct this. So since Vickers is such a great guy and released his dataset to the public we can map Vickers dataset in the same manner as Williams. Vickers has a total of 862 runners in his dataset (including what I believe is a slower median population meaning it is more representative of the US population of marathon runners) that have matching HM and M condition races (and if not matching than an adjustment was used).

View attachment 302771

Hooray! I'd say for the most part the datasets follow a similar trend. Not the same, but similar.

So the initial conclusion was 1.15 is a better predictor R for HM to M than is 1.06. It does split the middle of the data set (with 47% on both sides). So better. Williams dataset says the midpoint is 1.15 with a 25-75% range of 1.10 to 1.19 and Vickers dataset says the midpoint is 1.13 with a 25-75% range of 1.09-1.17.

So for a 2:00 HM runner, what does that mean?

Rigel - traditional calculator (1.06) = M of 4:10:12
Williams - 1.15 = M of 4:26:18 (range of 4:17-4:33)
Vickers - 1.13 = M of 4:22:38 (range of 4:15-4:30)

Since you are likely to see a better performance in the marathon with a conservative start, this new value of around 1.13-1.15 looks good to me. Slower is better at the beginning so you can leave some room for error in the second half of the race. Go out too fast in the beginning and the risk of blowing up is much much higher.

The problems start to arise when he starts to parce the data apart to make other conclusions about training in general that leads to performance.

Does gender matter?

Matches what I've read before. Women are better pacers during a marathon (more even/negative splits and less positive splits, (or faster at the end)), hypothesized that women are better at burning fat then men, and hypothesized that women are better at dissipating heat than men. So if a woman and a man have equal HM times going into the M, the woman is more often than not going to beat the man.

So I agree with the conclusion.

Are faster runners better?

600.png


The bottom grey line represents the top 10% of runners with that HM time in each subset of data. So Williams pieced apart the dataset into secondary pools with HM times of 1:20, 1:25, 1:30, 1:35, 1:40, etc. Given the relative smoothness of the line we can tell this is the case. Remembering back, there are only 67 total runners with a 1.06 or less in the dataset of 1071. There are only 256 with a 1.10 or less. There appear to be 9 subsets of data. As would make sense, there are likely fewer runners in the dataset at 1:20-1:30, then there is at 1:50-2:00 (if this dataset is anything like a normal population of HM runners). So the data at the beginning of the line is probably based off very few runners.

The first thing that jumps out to me is that the relationship between HM time and R (for M) is pretty equal for the top 10% across all HM times. A 1:20 10% runner is around 1.06, but so is a 1:55 runner. And the difference between the two is quite small anywhere in-between.

So the variation of the mean is not coming from the top 10% becoming worse converters, but the bottom portion of the population as the HM time slows are getting worse at being converters. So the question would follow, what are the top 10% runners doing that are all near 1.06 across all HM times that the bottom 10% are not? Seems to suggest that regardless of HM time you can be a good converter if you're doing the right things in training. And those in the slower HM times tend to have more runners doing the wrong thing in training (hence bad converters).

What about training mileage?

600.png


So per Williams this graph is the "typical" amount of miles run by experienced marathon runners (not their first) going for a PR marathon attempt. This does not have to be the same dataset he used to create the previous graph, but rather a measuring stick he created. So this original dataset doesn't have to be correlated with success in any way or being a good converter.

So the graph on the surface tells a story that most of us know. The people with faster marathon finishing times run more miles. But you know me, I don't like to look at miles, I like duration. So if I were to standardize these mileages across each subset by either Marathon Pace or EB pace (which tends to be the average pace I schedule runners at or 1.12 times slower than MP), then what does the dataset look like?

View attachment 302774

A 2:20 runner runs 1200 miles in 16 weeks. The MP of 2:20 is a 5:21 min/mile. If the 2:20 runner were to average MP for the 16 weeks of training, then they would do 6:40 hours of training per week (or 106 hours total). If we instead used EB, then the 2:20 runner averages 7:28 hours per week. The 2:20 is clearly the outlier, because look at the other subsets of data. The 2:40, 3:00, 3:20, 3:40, 4:00, 4:20, and 4:40 all run about 5:00 hours (if at MP) or 5:30 hours (if at EB) per week. So on the surface the 2:40 to 4:40 runners would appear different, but when taking into account their relative training pace, they're all actually very similar. This comes down to training load and why I like to evaluate training plans by time moreso than mileage. Two runners doing 80% of training at easy with 9 hours of total running per week will be reaping similar training benefits regardless if one runs a 2:20 M and the other a 4:40 M.

For reference, the marathon training plans I write tend to be in the 7-8 hours average range for 16 weeks. So my plans are like the outliers in the 2:20 M time group.

600.png


This is a hard graph for me to interpret. Based on the shape and description, I believe this is a cumulative graph. Meaning that once a runner has been passed in the data set it continues to get counted. So a runner in the 1.06 success portion means that 12% of runners who have sufficient mileage achieve a 1.06. And 60% of runners with sufficient mileage achieve a 1.15 OR LESS. Since the graph does not go down EVER, I don't believe the interpretation of the graph is when r=1.15 is achieved 60% of runners with a 1.15 had sufficient mileage because for that to be the case the addition of insufficient and sufficient on the graph should always equal 100%.

Here's where the interpretation of the graph gets tricky for me. Going back up to the original dataset, there are 580 runners who achieved a 1.15 or better (or 54.19% of the dataset). A total of 60% of runners with sufficient mileage ran 1.15. So the sufficint mileage group and the total group are 60% vs 54.2%. Seems to me these are not very far off from each other. Using this information, I should be able to calculate the number of runners in the 1071 dataset with sufficient mileage and insufficient mileage. I'll save the math, but it comes down to 820 runners have sufficient and 251 runners had insufficient. That allows a 60% success rate in sufficient and 35% success rate in insufficient while maintaining a total of 580 runners in the total dataset.

So going back to 1.06 then, we have 67 total runners at 1.06 OR LESS. From the graph, approximately 12% of the sufficient group hits a 1.06 vs ~5% for insufficient. So, how does that look in the raw numbers? Well that's where I can't make sense of it. If 12% of 820 runners are successful at 1.06 OR LESS, then I've got 98.4 runners. But only 67 runners in the whole data set were successful at 1.06 or LESS. So my original interpretation can't be right, can it? Therefore, I'm confused on this one.

I believe the basic premise is correct, those who run more tend to be more successful. But I can't figure out how to interpret this graph.

What about long runs?

A common consideration for marathon training plans is the long run.

600.png


Can't say I've ever heard of the 5L = 100 mile rule of thumb. Where the 5 longest runs in a 16 week plan summed together equal over 100 miles is a good sign. Again, I standardized this information by time:

View attachment 302787

If a 2:20 runner does 110 miles, then they are averaging 22 miles per Long Run. If the pace is MP, then they are doing it in 1:57:33. If the pace is LR pace (roughly 8% slower than MP), then it's duration is 2:07. So the faster runners, tend to do less total duration on their longest run cumulatively over the course of the plan. Sounds about right to me. I'm of the mindset that the cutoff should be around 2:30 for a training run duration limit at LR pace. Seems like the runners doing 2:20-3:00 marathon times are in that range. And many of the runners doing up to 2:45+ are in the 3:20 or slower M time range. So faster runners are spending less total time in any single training run.

So where the amount of time spent training was near equal across the board, the same doesn't appear for the 5L evaluation. On a training plan like mine, where does 5L typically fall?

View attachment 302788

Since it's based on time, I've got the MP and LR paces for different paced M finish times. I then calculated the peak of training as 2:30 duration limit. So a 3:20 runner will max at 18 miles and a 4:40 runner at 13 miles. Then, I like to hit peak only twice during a plan and then reduce every previous "high" week by one mile. So for me, a 2:20 runner would be doing 124 miles as 5L (higher than the 110 from Williams dataset) and a 4:00 runner would do 70 miles (or far lower than the 95 miles in Williams dataset).

600.png


Now, since all of the runners 5Ls are pooled together, I can't evaluate this graph by duration. But I can point out something troubling to me. The grey lines again represent top and bottom 10%. I already showed reasonably well that my assumed dataset matched Williams graphed dataset. Yet, I estimate he has maybe 10 to 11 total runners out of 1071 above or at 1.30 R. This graph shows the bottom 10% of 85, 90, 95, 100, and 110 at or higher than 1.30. How can that possibly be when there are only 10 to 11 runners in this area? Another new dataset? Confused again.

What I do get from this graph is that a difference of 85 (17 mile avg) vs 100 (20 mile avg) yields an R difference of 1.15 vs 1.21. For a 2:00 HM runner, that's 4:26 vs 4:37 (4% diff). Not an insignificant difference, but not as big a difference as the "are faster runners better" difference which was more like 1.10 vs 1.20 from faster runners to slower runners (5-7% difference). So something other than 5L plays a bigger role in predicting good converters vs bad ones.

600.png


So I can take this graph one step further. Williams gives data on 16 week training mileage and 5L from 16 weeks. Which means I can calculate his subset data's % by Marathon time.

View attachment 302789

The 2:20 runners had a 5L of 110 and 16 week total of 1200 miles. Therefore, their %5L of total was 9.2%. So not only are the better converters around 10% of total, but so are the faster runners. It's possible then to think that if one were to train like a faster runner/better converter they could achieve a lower R (and better M time relative to HM performance). So balance is important. I preach that a ton. So it's not the total mileage of the 5L that matters near as much as the % of which 5L makes up the total plan. So spend less time on the long run, and more time spent training during the week.

So where do my plans fall?

View attachment 302790

As covered previously, a 5L for me for a 3:00 runner will be around 95 miles. They'll do about 7 hours of training on average regardless of current fitness level. Their pace will be around EB (1.12x slower than MP) as an average for the plan. Therefore, we can calculate the average mileage and total mileage for each subset underneath my scheme. This comes out to a nearly identical 11% 5L as a % of the total training mileage across the board. So my plans are closer to the R values of 1.06-1.07 (or my training plans are better representative of runners who tend to get faster M times relative to their HM times).

What about training pace?

In my book, it's pretty darn important. Pace matters more than mileage, because to me mileage is just a function of time and pace spent training.

600.png


Unsurprising to me, runners at the faster paces actually train far slower than final race pace. I hark on this all the time. It suggests that if someone were to slow down in training, they too might yield better race results (or be faster).

600.png


An interesting graph. I interpret this to mean that until your average is about 40 seconds slower than race pace, you are more likely to run slower than a 1.15 conversion then you are to run faster than it. Those who run too fast in training tend to be the ones who run worse relative performances against their HM times. So, train slower! Sure seems like somewhere between 40-70 seconds is a sweet spot. There aren't actually that many runners at 80+ seconds, but those who do are pretty successful relatively on achieving a less than 1.15 R value.

So what about my plans?

View attachment 302792

According to Williams, runners with a race pace of 6:00 tend to run on average 72 seconds slower. So they'd be doing about a 7:12 average. Those at 8:00 with 35 seconds slower, at 8:35. For me, my training plans nearly always equal EB which is 1.12x slower than MP. So a 6:00 runner would average 6:43 and a 8:00 runner a 8:58. So my time differential across the board falls between 40-72 seconds. Going back to the graph Williams presented and that just so happens to appear as the sweet spot for beating the R of 1.15 (or being a better converter and achieving a faster M relative to HM performance).

On being rested

And then there's this graph....

600.png


This appears to be saying that peak mileage is reached in "x" week of the 16 weeks. Not surprising to see that the tallest bar is 13 weeks of the traditional taper (3 weeks out). Using the y-axis I can determine how many runners peak at either 13, 14, or 15 weeks of the training plan. It is about 170. The total dataset is 1071 runners. Problem is, when I run through the numbers I only get ~680 total runners, not 1071 runners. Where did the rest of the data set go???

But even ignoring that, the alarming part is this. The traditional taper is 3 weeks. Some do 4 weeks and others 2 weeks. But in this specific dataset there are a huge (roughly 65%) number of runners doing the taper at 5 weeks out or MORE??? Some hitting highest mileage week in Week 1? And not just a few people, but 3% of this graph's population. That seems astoundingly high. Maybe they did 10 miles every week for 16 weeks and thus hit their max mileage in week 1, but that seems odd to me from a dataset standpoint.

Conclusions

The conclusions we can draw from this:

-If HM performance is equal, women are likelier to finish with a faster M time than men.
-Runners of all abilities are capable of a 1.06 or less, and roughly the top 10% of all subgroups from 1:20 HM'ers to 2:00 HM'ers were roughly the same R value (or relative performance).
-Faster runners are better converters with a lower R overall average. Makes sense then why Rigel came up with 1.06 since the elite runners available to him would have been a similar pool to the faster runners in Williams dataset.
-Runners on the slower side of the HM performances tend to have more variability as a group because of the bad converters in their groups, not because of the lack of good converters. So more people on the slower side of HM performance training inappropriately for marathon performance.
-Roughly 5:00 to 5:30 hours per week on average for a marathon training plan is considered "typical" or "sufficient" by Williams.
-Those who run more than 5:00-5:30 hours per week are more successful at being good converters than are runners who run less than 5:00-5:30 hours per week.
-Those who do 5L around 100 barely appear different than those around lesser or higher numbers. The 5L would suggest it is lower on the predictive nature than other variables.
-Those who have 5L be a lower % of total mileage from 16 weeks tend to be the best converters. The faster runners also tend to be the ones with lower %5L values. Relying less on the long runs and more balance yields a better relative performance.
-Those who train at 40-80 seconds slower than race pace more often than not will be a good converter and have a R less than 1.15.

For my marathon training plans:

-The training load I schedule (around 7 hours per week) is sufficient (above 5-5.5 hrs) and is most like a 2:20 marathon runner's training plan.
-Almost none of my training plans would hit the 100 mile rule of thumb 5L. Most would be far far lower. The data suggests this is a minimal variable compared to other things.
-The %5L of training plans is a very good predictor of being a good converter. My plans are about 11% 5L of the total regardless of ability levels. The best converters (1.06-1.07) are around 9-10%. The worst converters (1.17-1.18) are around 20-21%.
-My training plans average pace is between 40-70 seconds depending on one's relative fitness. The point at which you are more likely to achieve a conversion better than 1.15 than not, is between 40-70 seconds. Or exactly where I schedule my paces.

This explains why most of my marathon training plans yield a final marathon time very close to my prediction. They check off all the boxes for optimal race day performance based on Williams conclusions. My predictions between HM and M performance is 4% or almost exactly a value of 1.06. So my runners tend to achieve in the top 25% of relative performances or at around 1.10 or less for an R value.

So a good marathon plan is:
-Over 5-5.5 hrs in duration per week on average for 16 weeks.
-Has a 5L% of 9-11%. So if you do 100 miles as 5L (or five 20 milers), then you better be doing 1000 miles in the 16 weeks of training (or 63 miles per week on average). The more you diverge from this, the worse your HM conversion becomes. Although, you can still be successful at a lower 5L like 60 miles if the 5L% is still in the 9-11% range (or 600 miles total and 38 miles per week) as long as that duration is over 5-5.5 hours for your paces.
-Has you training at roughly 40-80 seconds slower on average for the plan than marathon race pace.

That was fun! Alright, that's what I see. What do you think?

Wow, very interesting read!! I can personally attest to the fact that Billy harks on slowing down, and for very good reason :teacher:
 


Question -
I know a lot of people (myself included) use McMillan to predict race performance. Is it known what McMillan uses? Is it Rigel's 2^1.06? Some other published formula? Or is that just one of those secrets that we'll never know? (I have my own issues with McMillan, but that's more because the site doesn't have a publicly available developer api ... I have a very one-track mind sometimes)
 
Catching up so sorry on the stress fracture, what a bummer & crazy you rocked Dopey with it! Here's pixie dust healing your way.
oh and love the chalkboard pics with your daughter! I love when my clients have those for milestone photo sessions.
 
I've been on a calcium-channel blocker for some years, and even though my resting HR is about 50 BPM, I haven't noticed significant problems with it. Obviously, you'll have to see. Also, check with the doc, because calcium-channel blockers usually work relatively quickly and don't depend on long-term exposure/build-up. What that means, is that if you know there's a cold snap coming through in the next week, you can decide to start taking them for that week to help prevent an attack, and then stop after that. This past summer, since it was so warm, I stopped taking mine for a few months and then started up when the temperatures started getting colder again. In that sense, they are relatively flexible and not really a long-term commitment like some meds. (Also during the time I was off, I was curious about if going off the meds would affect my RHR, and no noticeable affect.)

Yea, the pharmacokinetics and pharmacodynamics suggest the following:

-The peak plasma occurrence after a single dose is 6-8 hours.
-Half life of the drug is 40-60 hours.
-Metabolized mostly by the liver.
-Steady state of the drug is 7-10 days. So it takes roughly 7-10 days of every day dosing before the effects of the dosage level are reached consistently.
-It also takes 7-10 days for the effects of the daily dose to wear off after discontinuation.

The only major risk for stopping the drug for people like us prescribed it for Raynaud's and not a heart condition, is if we were to develop a heart condition while on it. The condition may go unnoticed until you suddenly stop taking it leading to a rise in BP and possible complications with whatever heart issue you might have developed. So, not impossible for someone like me who has a family history of heart disease to be in the more risky pool of Raynaud's users and stopping/starting the meds.

Glad you have things figured out overall. I had never heard of the petechiae related to Raynaud's, so that is interesting in the scientific sense. A lot of the long-term living with it just means figuring out what works for you. It will give you something to experiment with while your stress fracture is healing - I expect graphs and tables to be posted. :) Perhaps you will have to develop a scale to indicate the severity of an attack?

Thanks! The scale and table already exist! It's the RCS or Raynaud's Condition Score table which is commonly used in clinical trials with Raynaud's.

http://fizyolcek.com/wp-content/uploads/2017/02/flier.pdf

Wow, very interesting read!! I can personally attest to the fact that Billy harks on slowing down, and for very good reason :teacher:

::yes:: Daily!

Question -
I know a lot of people (myself included) use McMillan to predict race performance. Is it known what McMillan uses? Is it Rigel's 2^1.06? Some other published formula? Or is that just one of those secrets that we'll never know? (I have my own issues with McMillan, but that's more because the site doesn't have a publicly available developer api ... I have a very one-track mind sometimes)

I know I looked into this before, but couldn't find the prior post on it. McMillan is indeed a "secret" calculator based on his own data set of over a million samples. It is not Riegel's 1.06. But I was curious based off building the excel data on the other R values to see where McMillan fell. Was it variable? Was it a set number? Was it all over the place?

Screen Shot 2018-02-16 at 2.46.53 PM.png

It's just 1.07 for R instead of 1.06... That was too easy. So much for a secret...
 


Catching up so sorry on the stress fracture, what a bummer & crazy you rocked Dopey with it! Here's pixie dust healing your way.

Thanks! It brings a smile to my face that my body allowed me to do what I did at Dopey and never gave me a hint of a problem. I'm very thankful. Now I'm repaying the body with some time off.

oh and love the chalkboard pics with your daughter! I love when my clients have those for milestone photo sessions.

Thanks! It'll be a nice memory for years to come. Wonder at what age she won't want to do it anymore?
 
Conclusions

The conclusions we can draw from this:

-If HM performance is equal, women are likelier to finish with a faster M time than men.
-Runners of all abilities are capable of a 1.06 or less, and roughly the top 10% of all subgroups from 1:20 HM'ers to 2:00 HM'ers were roughly the same R value (or relative performance).
-Faster runners are better converters with a lower R overall average. Makes sense then why Rigel came up with 1.06 since the elite runners available to him would have been a similar pool to the faster runners in Williams dataset.
-Runners on the slower side of the HM performances tend to have more variability as a group because of the bad converters in their groups, not because of the lack of good converters. So more people on the slower side of HM performance training inappropriately for marathon performance.
-Roughly 5:00 to 5:30 hours per week on average for a marathon training plan is considered "typical" or "sufficient" by Williams.
-Those who run more than 5:00-5:30 hours per week are more successful at being good converters than are runners who run less than 5:00-5:30 hours per week.
-Those who do 5L around 100 barely appear different than those around lesser or higher numbers. The 5L would suggest it is lower on the predictive nature than other variables.
-Those who have 5L be a lower % of total mileage from 16 weeks tend to be the best converters. The faster runners also tend to be the ones with lower %5L values. Relying less on the long runs and more balance yields a better relative performance.
-Those who train at 40-80 seconds slower than race pace more often than not will be a good converter and have a R less than 1.15.

For my marathon training plans:

-The training load I schedule (around 7 hours per week) is sufficient (above 5-5.5 hrs) and is most like a 2:20 marathon runner's training plan.
-Almost none of my training plans would hit the 100 mile rule of thumb 5L. Most would be far far lower. The data suggests this is a minimal variable compared to other things.
-The %5L of training plans is a very good predictor of being a good converter. My plans are about 11% 5L of the total regardless of ability levels. The best converters (1.06-1.07) are around 9-10%. The worst converters (1.17-1.18) are around 20-21%.
-My training plans average pace is between 40-70 seconds depending on one's relative fitness. The point at which you are more likely to achieve a conversion better than 1.15 than not, is between 40-70 seconds. Or exactly where I schedule my paces.

This explains why most of my marathon training plans yield a final marathon time very close to my prediction. They check off all the boxes for optimal race day performance based on Williams conclusions. My predictions between HM and M performance is 4% or almost exactly a value of 1.06. So my runners tend to achieve in the top 25% of relative performances or at around 1.10 or less for an R value.

So a good marathon plan is:
-Over 5-5.5 hrs in duration per week on average for 16 weeks.
-Has a 5L% of 9-11%. So if you do 100 miles as 5L (or five 20 milers), then you better be doing 1000 miles in the 16 weeks of training (or 63 miles per week on average). The more you diverge from this, the worse your HM conversion becomes. Although, you can still be successful at a lower 5L like 60 miles if the 5L% is still in the 9-11% range (or 600 miles total and 38 miles per week) as long as that duration is over 5-5.5 hours for your paces.
-Has you training at roughly 40-80 seconds slower on average for the plan than marathon race pace.

That was fun! Alright, that's what I see. What do you think?

Now I'm going to have to sit down and evaluate your data evaluating his data... :surfweb:

I think we aligned pretty well on takeaways. I did have this feeling that a few of the graphs were on different scales. I wondered if maybe the online logging wasn't 100% complete for all runners, so he had differing data set sizes for some of his sections.

My high level takeaways were:
-Allocating too high of a percentage of your weekly miles to the long run is not a recipe for success. Completing a 20 miler is great if you are targeting to finish the marathon, but if you are training for good time conversion, the total effort per week is critical
-There is an increasing floor of minimal mileage per week needed, as goal time drops, to achieve lower R values. This is interesting to me, and I need to chew on it more. This would seem to say that taking a mileage based plan that I used in the past, and adjusting to faster paces alone, may not be a successful way to PR. I've done this before, but I have had less success with it recently. I've been debating if it is just age related, but it could be that I need mileage/duration adjustments more than pace adjustments.
-Slow down on the long run! I like to run about 60 seconds per mile slower than MP for long runs. This is yet another data set that shows slowing down really is important (Channeling Doc "You have to turn right to go left"). I think I'm good here, but it is just another reinforcing reminder to stay slow.
 
Not to make this all about me, but... based on this dataset it seems likely that my marathon time will underestimate my speed at shorter distances using standard calculators. So should I be doing a time trial at a shorter distance rather than basing my training on paces that could be inaccurate?
 
I love it! Reverse engineering at its best :thumbsup2

Thanks!

Now I'm going to have to sit down and evaluate your data evaluating his data... :surfweb:

popcorn::

I think we aligned pretty well on takeaways. I did have this feeling that a few of the graphs were on different scales. I wondered if maybe the online logging wasn't 100% complete for all runners, so he had differing data set sizes for some of his sections.

It's certainly possible. But a good article or paper should discuss things like a changing dataset and why. Don't leave it to the reader to do some serious leg work to figure it out on there own. It can lead to mistrust from the audience about any conclusions from the paper. The one that confuses me the most is the "Average Rigel number based on 5L". I mean that's a lot of values about 1.40. The original dataset graph should extend to 1.40 if there are values out there. Can't see how they'd get excluded from that original dataset if they were above 10% of the total picture.

-Allocating too high of a percentage of your weekly miles to the long run is not a recipe for success. Completing a 20 miler is great if you are targeting to finish the marathon, but if you are training for good time conversion, the total effort per week is critical

Agreed! Of course, I'd say that the usual runner "targeting a finish" are your typical first time marathoners which I'd recommend be the last people attempting 20 milers (given they tend to be on the slower side of the spectrum of all marathoners).

-There is an increasing floor of minimal mileage per week needed, as goal time drops, to achieve lower R values. This is interesting to me, and I need to chew on it more. This would seem to say that taking a mileage based plan that I used in the past, and adjusting to faster paces alone, may not be a successful way to PR. I've done this before, but I have had less success with it recently. I've been debating if it is just age related, but it could be that I need mileage/duration adjustments more than pace adjustments.

Well you know how I like to look at things- duration. As an example, say you were doing a 40 mile a week plan and a 2:00 HM runner.

The average pace for a training plan of a 2:00 HM would be 10:47 min/mile (based on 1.17 times higher than HMP (9:09)). That's 7:11 hours of training per week. That's absolutely a healthy amount of training and likely will yield between a 4-6% improvement in time in most cases. Sometimes as high as 10-12%. So that 2:00 HM at the end of a 16 cycle would usually be as fast as a 1:52-1:55. Do that cycle again and they do about 6:44 hours of training per week if they re-used the same 40 mile a week plan with current fitness pacing. That's 30 min less per week (or 8 hours over the course of all the training). Now the improvement might not be 4-6% anymore because the training load is now lower. So maybe 3-5%. Now a 1:47 HM time. So first improvement was 8 minutes, but the second was 5 minutes. Now re-use again... Now down to 6:25 hours per week (almost an hour less per week than when you started). Maybe the improvement shifts down again to 2-4%. Now at 1:43.

Now if the same runner had kept training at 7:11 instead of going 7:11, 6:44, 6:25 in three consecutive cycles they could have been at 6% improvement each of the three times instead of 6, 5, 4. What's the difference in the end? A 1:43 HM on an unadjusted plan versus a 1:39. That's just the difference in about a year's time.

These are all obviously just made up numbers and a simplistic view, but it does show as an example what I believe would occur with a reduced training load (based on duration). That's why I believe that pace and mileage move in unison. If you get faster, then you need to do more mileage. But in essence you can do the same amount of training load. You just do more mileage because you're faster at a set duration.

-Slow down on the long run! I like to run about 60 seconds per mile slower than MP for long runs. This is yet another data set that shows slowing down really is important (Channeling Doc "You have to turn right to go left"). I think I'm good here, but it is just another reinforcing reminder to stay slow.

Agreed!

Not to make this all about me, but... based on this dataset it seems likely that my marathon time will underestimate my speed at shorter distances using standard calculators. So should I be doing a time trial at a shorter distance rather than basing my training on paces that could be inaccurate?

That's fair and it almost certainly will. But we also took a stab at estimating a HM time of 1:56 even though you ran a 2:03 HM in hot conditions. This is part of the fluidity of the training plan. Starting tomorrow is the beginning of R paces. You keep giving me feedback and I can figure out whether an adjustment is necessary. BUT, and this is a BIG BUT. You do not adjust the paces until I figure out whether it is justified. If I have it scheduled for 56 second R paces. Then I want you to report back that you ran eight 56 R paces in a row. And then you ran 1:24 300s without issue. The goal is to nail the scheduled paces, not to try and beat them. Beating them may yield inappropriate training.

I have seen time and again that training slightly too slow will not hold you back. A 10k is roughly 90% aerobic. And training slightly too slow will still be aerobic in nature. The pace spectrum on the slow side is much wider. But training too fast is certain to cause issues and lead to less than ideal racing results. The pace spectrum on the faster side is much much tighter. So being a few seconds too fast on a R can be completely changing the workout. No reason to believe you'll be stuck at a 52 min 10k, and you could definitely run a 47 min 10k instead come May. It comes with time. Still got many weeks ahead to figure out what is best.

A time trial is something I've been know to throw in randomly mid-training if I feel we'll see a big difference based on the previously setup paces. So just because it isn't on the original schedule doesn't mean it won't happen.

So my biggest advice is to continue to give me feedback and I can figure out whether we need to step it up. But stick to the schedule in the meantime. I'm excited to see you absolutely crush those paces!
 
That is really helpful, thanks! You've mentioned that the R repeats need to be +/- 1 second, so my concern was, if the pace is not challenging enough, is the training ineffective? But now I get the message that too fast is ineffective but slower paces still add value. I'm totally overthinking things, as I'm sure any amount of speed training will make a big difference - I did basically none in 2017!

I have to admit that I did my first R paced run already :rolleyes1. I will still execute the week as planned, but in a different order due to some logistical issues. I did this run on the treadmill as winter running conditions weren't safe. The pace felt much faster than I'm used to, but not hard from a breathing perspective - HR maxed out at 159 with chest strap (but of course the repeats were only 56 seconds). If outdoor conditions aren't good this week I will try the track to see what the run is like when I have to manage the pacing myself.
 
That is really helpful, thanks! You've mentioned that the R repeats need to be +/- 1 second, so my concern was, if the pace is not challenging enough, is the training ineffective? But now I get the message that too fast is ineffective but slower paces still add value. I'm totally overthinking things, as I'm sure any amount of speed training will make a big difference - I did basically none in 2017!

Let's say the 56 second R (7:28 min/mile) was indeed too slow. Let's say right now you are at 1:44 HM PR fitness from 8 years ago right now instead. That 7:28 pace would be slightly slower than 5k pace. So yea there can be a big difference between a 56 second 5k workout and a 56 second mile workout. But let's just see how things proceed from here. I'm more than willing to adjust the paces. I always say 8 weeks out is the last time I'll change paces. So let's see how things go from here to mid-March. The plan is meant to be easier right now and progressively get harder. So the worst thing I can do for you is adjust the paces too soon.

I have to admit that I did my first R paced run already :rolleyes1. I will still execute the week as planned, but in a different order due to some logistical issues. I did this run on the treadmill as winter running conditions weren't safe. The pace felt much faster than I'm used to, but not hard from a breathing perspective - HR maxed out at 159 with chest strap (but of course the repeats were only 56 seconds). If outdoor conditions aren't good this week I will try the track to see what the run is like when I have to manage the pacing myself.

Yea, HR monitor is useless for a R paced run. Not enough time at pace to reach any type of measurable value to evaluate performance.

If you mess with the plan, remember no two hard workouts (like LR, R, T, I) on consecutive days. If you end up missing an easy day, then you have to replace the next hard day with that easy day.

Running the speed workouts on a treadmill isn't ideal, but it's certainly better than getting hurt with unsafe outside conditions.

I’m so glad all I have to do is follow my plan! All that math....oh my!!! Props to you coach!!

Certainly a lot of work on the back end. But the goal is to make the plan really simple for the user (you). Do x pace at y duration. And that's it. But there's so much more than goes into it than that. All the math!
 
Lol - well, I can promise you that I am nowhere near 1:44 HM fitness, so we don't have that to worry about :)!

I figured I could get away with moving a run this week since I had only 3 easy and 1 hard. But I will definitely be careful not to mess up the hard/easy balance.

I am suuuuper excited to be moving into phase 2 this week after 3 weeks of slow running :teeth:.
 
I had to check in here given your lack of presence on Strava! I'm glad you got diagnosed. Sounds like an awkward enough stress fracture.

Cycling is definitely a good way of getting some workouts in if it's do-able for you. When I injured my calf a while back out of desperation I spent a bit of time on an elliptical and while it felt a lot closer to running, there was definitely slight bit more strain on the bits that really need to be rested. Swimming can be good too (though I'm terrible at it myself). Just don't get sucked into considering triathlons as a result :D
 
I had to check in here given your lack of presence on Strava! I'm glad you got diagnosed. Sounds like an awkward enough stress fracture.

Cycling is definitely a good way of getting some workouts in if it's do-able for you. When I injured my calf a while back out of desperation I spent a bit of time on an elliptical and while it felt a lot closer to running, there was definitely slight bit more strain on the bits that really need to be rested. Swimming can be good too (though I'm terrible at it myself). Just don't get sucked into considering triathlons as a result :D

Thanks! Hoping to get a gym membership tomorrow and start hitting the bike. Not sure a triathlon is in my immediate future. I love running too much, but I guess we'll see.
 

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